Estimation of ultimate bearing capacity of bored piles using machine learning models

IF 2.4 Q2 GEOSCIENCES, MULTIDISCIPLINARY VIETNAM JOURNAL OF EARTH SCIENCES Pub Date : 2022-05-28 DOI:10.15625/2615-9783/17177
Binh Thai Pham, Dam Duc Nguyen, Quynh-Anh Bui Thi, Manh Duc Nguyen, Thanh Tien Vu, Indra Prakash
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引用次数: 3

Abstract

The ultimate bearing capacity of bored piles is an essential parameter in foundation design of structure. In the present study, three Machine Learning (ML) methods namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized to estimate bearing capacity of bored piles based on limited engineering parameters of pile and soil obtained from 75 test sites in Vietnam. These parameters include pile diameter, pile length, tensile strength of main longitudinal steel bar, compressive strength of concrete, average SPT index at the tip of the pile, average SPT index at the pile body. Validation of the methods was verified using standard statistical metrics namely Root Mean Square Error (RMSE) and Correlation coefficient (R). The results show that all the proposed models have good potential in predicting correctly bearing capacity of bored piles on training data (R>0.93) and on testing data (R>0.88) but performance of the SVM model is the best (R:0.985 (training) and R:0.958 (testing). Thus SVM model can be used for the accurate prediction of ultimate bearing capacity of bored piles for proper designing of the civil engineering structure foundation.
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基于机器学习模型的钻孔灌注桩极限承载力估算
钻孔灌注桩的极限承载力是结构基础设计中的一个重要参数。本文采用自适应神经模糊推理系统(ANFIS)、支持向量机(SVM)和人工神经网络(ANN)三种机器学习(ML)方法,基于越南75个试验点的有限桩土工程参数,对钻孔灌注桩的承载力进行了估计。这些参数包括桩径、桩长、主纵钢筋抗拉强度、混凝土抗压强度、桩端平均SPT指数、桩身平均SPT指数。采用标准统计指标均方根误差(RMSE)和相关系数(R)验证了方法的有效性。结果表明,所提出的模型在训练数据(R>0.93)和测试数据(R>0.88)上都有很好的预测钻孔灌注桩承载力的潜力,但SVM模型的性能最好(R:0.985(训练)和R:0.958(测试))。利用支持向量机模型可以准确预测钻孔灌注桩的极限承载力,为土木工程结构基础的合理设计提供依据。
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来源期刊
VIETNAM JOURNAL OF EARTH SCIENCES
VIETNAM JOURNAL OF EARTH SCIENCES GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
3.60
自引率
20.00%
发文量
0
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